More is different: Reconstituting complexity in microtubule regulation.

Microtubules are dynamic cytoskeletal filaments that undergo stochastic switching between phases of polymerization and depolymerization-a behavior known as dynamic instability. Many important cellular processes, including cell motility, chromosome segregation, and intracellular transport, require complex spatiotemporal regulation of microtubule dynamics. This coordinated regulation is achieved through the interactions of numerous microtubule-associated proteins (MAPs) with microtubule ends and lattices. Here, we review the recent advances in our understanding of microtubule regulation, focusing on results arising from biochemical in vitro reconstitution approaches using purified multiprotein ensembles. We discuss how the combinatory effects of MAPs affect both the dynamics of individual microtubule ends, as well as the stability and turnover of the microtubule lattice. In addition, we highlight new results demonstrating the roles of protein condensates in microtubule regulation. Our overall intent is to showcase how lessons learned from reconstitution approaches help unravel the regulatory mechanisms at play in complex cellular environments.

Differential expression of single-cell RNA-seq data using Tweedie models.

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.

Predictive signature development based on maximizing the area between receiver operating characteristic curves.

Development of marker signatures to predict treatment benefits for a new therapeutic is an important scientific component in advancing the drug discovery and is an important first step toward the goal of precision medicine. In this article, we focus on developing an algorithm to search for optimal linear combination of markers that maximizes the area between two receiver operating characteristic curves of the new therapeutic and the control groups without assuming any parametric model. We further generalize the proposed algorithm for predictive signature development to maximize the difference of Harrel's C-index of the new therapeutic and the control groups when the outcome of interest is time-to-event. The performance of this proposed method is evaluated and compared to existing methods via simulations and real clinical trial data.

Spatio-temporal regulation of nuclear division by Aurora B kinase Ipl1 in Cryptococcus neoformans.

The nuclear division takes place in the daughter cell in the basidiomycetous budding yeast Cryptococcus neoformans. Unclustered kinetochores gradually cluster and the nucleus moves to the daughter bud as cells enter mitosis. Here, we show that the evolutionarily conserved Aurora B kinase Ipl1 localizes to the nucleus upon the breakdown of the nuclear envelope during mitosis in C. neoformans. Ipl1 is shown to be required for timely breakdown of the nuclear envelope as well. Ipl1 is essential for viability and regulates structural integrity of microtubules. The compromised stability of cytoplasmic microtubules upon Ipl1 depletion results in a significant delay in kinetochore clustering and nuclear migration. By generating an in silico model of mitosis, we previously proposed that cytoplasmic microtubules and cortical dyneins promote atypical nuclear division in C. neoformans. Improving the previous in silico model by introducing additional parameters, here we predict that an effective cortical bias generated by cytosolic Bim1 and dynein regulates dynamics of kinetochore clustering and nuclear migration. Indeed, in vivo alterations of Bim1 or dynein cellular levels delay nuclear migration. Results from in silico model and localization dynamics by live cell imaging suggests that Ipl1 spatio-temporally influences Bim1 or/and dynein activity along with microtubule stability to ensure timely onset of nuclear division. Together, we propose that the timely breakdown of the nuclear envelope by Ipl1 allows its own nuclear entry that helps in spatio-temporal regulation of nuclear division during semi-open mitosis in C. neoformans.

Mechanics of Multicentrosomal Clustering in Bipolar Mitotic Spindles.

To segregate chromosomes in mitosis, cells assemble a mitotic spindle, a molecular machine with centrosomes at two opposing cell poles and chromosomes at the equator. Microtubules and molecular motors connect the poles to kinetochores, specialized protein assemblies on the centromere regions of the chromosomes. Bipolarity of the spindle is crucial for the proper cell division, and two centrosomes in animal cells naturally become two spindle poles. Cancer cells are often multicentrosomal, yet they are able to assemble bipolar spindles by clustering centrosomes into two spindle poles. Mechanisms of this clustering are debated. In this study, we computationally screen effective forces between 1) centrosomes, 2) centrosomes and kinetochores, 3) centrosomes and chromosome arms, and 4) centrosomes and cell cortex to understand mechanics that determines three-dimensional spindle architecture. To do this, we use the stochastic Monte Carlo search for stable mechanical equilibria in the effective energy landscape of the spindle. We find that the following conditions have to be met to robustly assemble the bipolar spindle in a multicentrosomal cell: 1) the strengths of centrosomes' attraction to each other and to the cell cortex have to be proportional to each other and 2) the strengths of centrosomes' attraction to kinetochores and repulsion from the chromosome arms have to be proportional to each other. We also find that three other spindle configurations emerge if these conditions are not met: 1) collapsed, 2) monopolar, and 3) multipolar spindles, and the computational screen reveals mechanical conditions for these abnormal spindles.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany.

In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle.

Patient subgroup identification for clinical drug development.

Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.

A simple one pot purification of bacterial amylase from fermented broth based on affinity toward starch-functionalized magnetic nanoparticle.

Surface-functionalized adsorbant particles in combination with magnetic separation techniques have received considerable attention in recent years. Selective manipulation on such magnetic nanoparticles permits separation with high affinity in the presence of other suspended solids. Amylase is used extensively in food and allied industries. Purification of amylase from bacterial sources is a matter of concern because most of the industrial need for amylase is met by microbial sources. Here we report a simple, cost-effective, one-pot purification technique for bacterial amylase directly from fermented broth of Bacillus megaterium utilizing starch-coated superparamagnetic iron oxide nanoparticles (SPION). SPION was prepared by co-precipitation method and then functionalized by starch coating. The synthesized nanoparticles were characterized by transmission electron microscopy (TEM), a superconducting quantum interference device (SQUID, zeta potential, and ultraviolet-visible (UV-vis) and Fourier-transform infrared (FTIR) spectroscopy. The starch-coated nanoparticles efficiently purified amylase from bacterial fermented broth with 93.22% recovery and 12.57-fold purification. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) revealed that the molecular mass of the purified amylase was 67 kD, and native gel showed the retention of amylase activity even after purification. Optimum pH and temperature of the purified amylase were 7 and 50°C, respectively, and it was stable over a range of 20°C to 50°C. Hence, an improved one-pot bacterial amylase purification method was developed using starch-coated SPION.

Surface-functionalized gold nanoparticles mediate bacterial transformation: a nanobiotechnological approach.

Transformation of bacteria is an important step in molecular biology. Viral and non-virus-based gene delivery techniques, including chemical/biological and physical approaches, have been applied to bacterial, mammalian and plant cells. E. coli is not competent to take up DNA; hence, different methods are used to incorporate plasmid DNA. A novel method has been developed using glutathione-functionalized gold nanoparticles to mediate transformation of plasmid DNA (pUC19) into E. coli DH5α that does not require the preparation of competent cells. The glutathione-functionalized gold nanoparticles acted as a vector and facilitated the entry of DNA into the host cell. The method also gave a higher transformation efficiency (4.2 × 10(7)/µg DNA) compared to 2.3 × 10(5)/µg DNA using the conventional CaCl2-mediated method. It was also non-toxic to the bacterium making it suitable for biotechnological applications.

Potential application of superparamagnetic nanoparticles for extraction of bacterial genomic DNA from contaminated food and environmental samples.

BACKGROUND: Isolation of high-molecular-weight DNA is essential for many molecular biology applications. Owing to the presence of polymerase chain reaction (PCR) inhibitors, there is a scarcity of suitable protocols for PCR-ready DNA extraction from food and natural environments. The conventional chemical methods of DNA extraction are time consuming and laborious and the yield is very low. Thus the aim of this research was to develop a simple, rapid, cost-effective method of genomic DNA extraction from food (milk and fruit juice) and environmental (pond water) samples and to detect bacterial contaminants present in those samples. RESULTS: This approach is efficient for both Gram-positive and Gram-negative bacteria from all the studied samples. Herein super paramagnetic bare iron oxide nanoparticles were implemented for bacterial genomic DNA isolation. The method was also compared to the conventional phenol-chloroform method in the context of quality, quantity and timing process. This method took only half an hour or less to obtain high-molecular-weight purified DNA from minimum bacterial contamination. Additionally, the method was directly compatible to PCR amplification. CONCLUSION: The problem of availability of suitable generalized methods for DNA isolation from various samples including food and environmental has been solved by a nanobiotechnological approach that may prove to be extremely useful in biotechnological applications.

Microtubule search-and-capture model evaluates the effect of chromosomal volume conservation on spindle assembly during mitosis.

Variation in the chromosome numbers can arise from the erroneous mitosis or fusion and fission of chromosomes. While the mitotic errors lead to an increase or decrease in the overall chromosomal substance in the daughter cells, fission and fusion keep this conserved. Variations in chromosome numbers are assumed to be a crucial driver of speciation. For example, the members of the muntjac species are known to have very different karyotypes with the chromosome numbers varying from 2n=70+3B in the brown brocket deer to 2n=46 in the Chinese muntjac and 2n=6/7 in the Indian muntjac. The chromosomal content in the nucleus of these closely related mammals is roughly the same and various chromosome fusion and fission pathways have been suggested as the evolution process of these karyotypes. Similar trends can also be found in lepidoptera and yeast species which show a wide variation of chromosome numbers. The effect of chromosome number variation on the spindle assembly time and accuracy is still not properly addressed. We computationally investigate the effect of conservation of the total chromosomal substance on the spindle assembly during prometaphase. Our results suggest that chromosomal fusion pathways aid the microtubule-driven search and capture of the kinetochore in cells with monocentric chromosomes. We further report a comparative analysis of the site and percentage of amphitelic captures, dependence on cell shape, and position of the kinetochore in respect to chromosomal volume partitioning.

CLASPs stabilize the pre-catastrophe intermediate state between microtubule growth and shrinkage.

Cytoplasmic linker-associated proteins (CLASPs) regulate microtubules in fundamental cellular processes. CLASPs stabilize dynamic microtubules by suppressing microtubule catastrophe and promoting rescue, the switch-like transitions between growth and shrinkage. How CLASPs specifically modulate microtubule transitions is not understood. Here, we investigate the effects of CLASPs on the pre-catastrophe intermediate state of microtubule dynamics, employing distinct microtubule substrates to mimic the intermediate state. Surprisingly, we find that CLASP1 promotes the depolymerization of stabilized microtubules in the presence of GTP, but not in the absence of nucleotide. This activity is also observed for CLASP2 family members and a minimal TOG2-domain construct. Conversely, we find that CLASP1 stabilizes unstable microtubules upon tubulin dilution in the presence of GTP. Strikingly, our results reveal that CLASP1 drives microtubule substrates with vastly different inherent stabilities into the same slowly depolymerizing state in a nucleotide-dependent manner. We interpret this state as the pre-catastrophe intermediate state. Therefore, we conclude that CLASPs suppress microtubule catastrophe by stabilizing the intermediate state between growth and shrinkage.

Evaluation of dose-dependent treatment effects after mid-trial dose escalation in biomarker, clinical, and cognitive outcomes for gantenerumab or solanezumab in dominantly inherited Alzheimer's disease.

Introduction: While the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) was ongoing, external data suggested higher doses were needed to achieve targeted effects; therefore, doses of gantenerumab were increased 5-fold, and solanezumab was increased 4-fold. We evaluated to what extent mid-trial dose increases produced a dose-dependent treatment effect. Methods: Using generalized linear mixed effects (LME) models, we estimated the annual low- and high-dose treatment effects in clinical, cognitive, and biomarker outcomes. Results: Both gantenerumab and solanezumab demonstrated dose-dependent treatment effects (significant for gantenerumab, non-significant for solanezumab) in their respective target amyloid biomarkers (Pittsburgh compound B positron emission tomography standardized uptake value ratio and cerebrospinal fluid amyloid beta 42), with gantenerumab demonstrating additional treatment effects in some downstream biomarkers. No dose-dependent treatment effects were observed in clinical or cognitive outcomes. Conclusions: Mid-trial dose escalation can be implemented as a remedy for an insufficient initial dose and can be more cost effective and less burdensome to participants than starting a new trial with higher doses, especially in rare diseases. Highlights: We evaluated the dose-dependent treatment effect of two different amyloid-specific immunotherapies.Dose-dependent treatment effects were observed in some biomarkers.No dose-dependent treatment effects were observed in clinical/cognitive outcomes, potentially due to the fact that the modified study may not have been powered to detect such treatment effects in symptomatic subjects at a mild stage of disease exposed to high (or maximal) doses of medication for prolonged durations.

Effect of iron oxide and gold nanoparticles on bacterial growth leading towards biological application.

BACKGROUND: Nanoparticle-metal oxide and gold represents a new class of important materials that are increasingly being developed for use in research and health related activities. The biological system being extremely critical requires the fundamental understanding on the influence of inorganic nanoparticles on cellular growth and functions. Our study was aimed to find out the effect of iron oxide (Fe3O4), gold (Au) nanoparticles on cellular growth of Escherichia coli (E. coli) and also try to channelize the obtained result by functionalizing the Au nanoparticle for further biological applications. RESULT: Fe3O4 and Au nanoparticles were prepared and characterized using Transmission electron microscopy (TEM) and Dynamic Light Scattering (DLS). Preliminary growth analysis data suggest that the nanoparticles of iron oxide have an inhibitory effect on E. coli in a concentration dependant manner, whereas the gold nanoparticle directly showed no such activity. However the phase contrast microscopic study clearly demonstrated that the effect of both Fe3O4 and Au nanoparticle extended up to the level of cell division which was evident as the abrupt increase in bacterial cell length. The incorporation of gold nanoparticle by bacterial cell was also observed during microscopic analysis based on which glutathione functionalized gold nanoparticle was prepared and used as a vector for plasmid DNA transport within bacterial cell. CONCLUSION: Altogether the study suggests that there is metal nanoparticle-bacteria interaction at the cellular level that can be utilized for beneficial biological application but significantly it also posses potential to produce ecotoxicity, challenging the ecofriendly nature of nanoparticles.

Remdesivir: Critical Clinical Appraisal for COVID 19 Treatment.

Remdesivir is presently been considered as 'molecule of hope' to curb the menace of COVID19. Non-availability of any USFDA approved drug has led to several attempt of drug-repurposing and development of new therapeutic molecules. However, Remdesivir has been found to be effective against a broad range of virus including SARS, MERS and COVID 19 through in-vitro studies. Several clinical research attempt are presently being conducted showing promising result yet not conclusive. This review summarized all such clinical trials to critically appraise the usage of Remdesivir against COVID 19 along with the publications related to the results of the clinical studies. The present regulatory aspect i. e. Emergency Use Authorization (EYA) and information of molecule and plausible mechanism is also dealt.

A Model-free Approach for Testing Association.

The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis, and inflammation, predictive of recurrence after lung resection in node-negative non-small cell lung cancer patients with tumor stage T2a or less. We propose an omnibus approach for testing association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile-based associations, even with outlier-prone and heavy-tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model-free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify preoperative serum biomarkers associated with non-small cell lung cancer recurrence in early stage patients.

Studying the progress of COVID-19 outbreak in India using SIRD model.

We explore a standard epidemiological model, known as the SIRD model, to study the COVID-19 infection in India, and a few other countries around the world. We use (a) the stable cumulative infection of various countries and (b) the number of infection versus the tests carried out to evaluate the model. The time-dependent infection rate is set in the model to obtain the best fit with the available data. The model is simulated aiming to project the probable features of the infection in India, various Indian states, and other countries. India imposed an early lockdown to contain the infection that can be treated by its healthcare system. We find that with the current infection rate and containment measures, the total active infection in India would be maximum at the end of June or beginning of July 2020. With proper containment measures in the infected zones and social distancing, the infection is expected to fall considerably from August. If the containment measures are relaxed before the arrival of the peak infection, more people from the susceptible population will fall sick as the infection is expected to see a threefold rise at the peak. If the relaxation is given a month after the peak infection, a second peak with a moderate infection will follow. However, a gradual relaxation of the lockdown started well ahead of the peak infection, leads to a nearly twofold increase of the peak infection with no second peak. The model is further extended to incorporate the infection arising from the population showing no symptoms. The preliminary finding suggests that random testing needs to be carried out within the asymptomatic population to contain the spread of the disease. Our model provides a semi-quantitative overview of the progression of COVID-19 in India, with model projections reasonably replicating the current progress. The projection of the model is highly sensitive to the choice of the parameters and the available data.

SEIRD model to study the asymptomatic growth during COVID-19 pandemic in India.

According to the current perception, symptomatic, presymptomatic and asymptomatic infectious persons can infect the healthy population susceptible to the SARS-CoV-2. More importantly, various reports indicate that the number of asymptomatic cases can be several-fold higher than the reported symptomatic cases. In this article, we take the reported cases in India and various states within the country till September 1, as the specimen to understand the progression of the COVID-19. Employing a modified SEIRD model, we predict the spread of COVID-19 by the symptomatic as well as asymptomatic infectious population. Considering reported infection primarily due to symptomatic, we compare the model predicted results with the available data to estimate the dynamics of the asymptomatically infected population. Our data indicate that in the absence of the asymptomatic infectious population, the number of symptomatic cases would have been much less. Therefore, the current progress of the symptomatic infection can be reduced by quarantining the asymptomatically infectious population via extensive or random testing. This study is motivated strictly toward academic pursuit; this theoretical investigation is not meant for influencing policy decisions or public health practices.

Mechanics of microtubule organizing center clustering and spindle positioning in budding yeast Cryptococcus neoformans.

The dynamic process of mitotic spindle assembly depends on multitudes of inter-dependent interactions involving kinetochores (KTs), microtubules (MTs), spindle pole bodies (SPBs), and molecular motors. Before forming the mitotic spindle, multiple visible microtubule organizing centers (MTOCs) coalesce into a single focus to serve as an SPB in the pathogenic budding yeast, Cryptococcus neoformans. To explain this unusual phenomenon in the fungal kingdom, we propose a "search and capture" model, in which cytoplasmic MTs (cMTs) nucleated by MTOCs grow and capture each other to promote MTOC clustering. Our quantitative modeling identifies multiple redundant mechanisms mediated by a combination of cMT-cell cortex interactions and inter-cMT coupling to facilitate MTOC clustering within the physiological time limit as determined by time-lapse live-cell microscopy. Besides, we screen various possible mechanisms by computational modeling and propose optimal conditions that favor proper spindle positioning-a critical determinant for timely chromosome segregation. These analyses also reveal that a combined effect of MT buckling, dynein pull, and cortical push maintains spatiotemporal spindle localization.